Visualisation des distributions du dataset Paradise avant nettoyage percentiles

Data Frame Summary

df

Dimensions: 18903 x 83
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 num_resurgences [integer]
Mean (sd) : 663752790 (4112558)
min ≤ med ≤ max:
585011309 ≤ 663761550 ≤ 667779650
IQR (CV) : 3428240 (0)
 18903 distinct values 0 (0.0%)
2 sexe [character]
1. F
2. M
9812(51.9%)
9091(48.1%)
 0 (0.0%)
3 age [integer]
Mean (sd) : 73.4 (17.2)
min ≤ med ≤ max:
18 ≤ 78 ≤ 107
IQR (CV) : 22 (0.2)
 90 distinct values 0 (0.0%)
4 Trt_Ara2 [integer]
Min : 0
Mean : 0.2
Max : 1
0:15450(81.7%)
1:3453(18.3%)
 0 (0.0%)
5 Trt_AntiAgr [integer]
Min : 0
Mean : 0.4
Max : 1
0:12285(65.0%)
1:6618(35.0%)
 0 (0.0%)
6 Trt_AntiCoag [integer]
Min : 0
Mean : 0.1
Max : 1
0:17843(94.4%)
1:1060(5.6%)
 0 (0.0%)
7 tt6h_broncho_beta2 [integer] 1 distinct value
0:18903(100.0%)
 0 (0.0%)
8 Trt_Bumetanide [integer]
Min : 0
Mean : 0
Max : 1
0:18736(99.1%)
1:167(0.9%)
 0 (0.0%)
9 Trt_BetaBloquants [integer]
Min : 0
Mean : 0.3
Max : 1
0:13116(69.4%)
1:5787(30.6%)
 0 (0.0%)
10 tt6h_diur [integer] 1 distinct value
0:18903(100.0%)
 0 (0.0%)
11 Trt_IEC [integer]
Min : 0
Mean : 0.2
Max : 1
0:14643(77.5%)
1:4260(22.5%)
 0 (0.0%)
12 Trt_Insuline [integer]
Min : 0
Mean : 0.1
Max : 1
0:16669(88.2%)
1:2234(11.8%)
 0 (0.0%)
13 OEDEME [integer]
Min : 0
Mean : 0.2
Max : 1
0:14253(75.4%)
1:4650(24.6%)
 0 (0.0%)
14 tt6h_broncho_parasymp [integer] 1 distinct value
0:18903(100.0%)
 0 (0.0%)
15 RONCHI [integer]
Min : 0
Mean : 0.1
Max : 1
0:16583(87.7%)
1:2320(12.3%)
 0 (0.0%)
16 RALES [integer]
Min : 0
Mean : 0.3
Max : 1
0:12376(65.5%)
1:6527(34.5%)
 0 (0.0%)
17 SIBILANTS [integer]
Min : 0
Mean : 0.2
Max : 1
0:15934(84.3%)
1:2969(15.7%)
 0 (0.0%)
18 Trt_Statine [integer]
Min : 0
Mean : 0.3
Max : 1
0:13991(74.0%)
1:4912(26.0%)
 0 (0.0%)
19 TURGJUG [integer]
Min : 0
Mean : 0
Max : 1
0:18841(99.7%)
1:62(0.3%)
 0 (0.0%)
20 tt6h_vni [integer]
Min : 0
Mean : 0.2
Max : 1
0:15558(82.3%)
1:3345(17.7%)
 0 (0.0%)
21 tth6_vasodil [integer]
Min : 0
Mean : 0.1
Max : 1
0:17487(92.5%)
1:1416(7.5%)
 0 (0.0%)
22 CoMorbidite_Non.Cardio.Vasculaire_Anemie [integer]
Min : 0
Mean : 0.1
Max : 1
0:17331(91.7%)
1:1572(8.3%)
 0 (0.0%)
23 CoMorbidite_CardioVasculaire__Angio [integer]
Min : 0
Mean : 0.2
Max : 1
0:15792(83.5%)
1:3111(16.5%)
 0 (0.0%)
24 CoMorbidite_CardioVasculaire_ICC [integer]
Min : 0
Mean : 0.3
Max : 1
0:13969(73.9%)
1:4934(26.1%)
 0 (0.0%)
25 CoMorbidite_CardioVasculaire__Arterite [integer]
Min : 0
Mean : 0.1
Max : 1
0:16824(89.0%)
1:2079(11.0%)
 0 (0.0%)
26 CoMorbidite_Non.Cardio.Vasculaire_Asthme [integer]
Min : 0
Mean : 0.1
Max : 1
0:17149(90.7%)
1:1754(9.3%)
 0 (0.0%)
27 CoMorbidite_CardioVasculaire__AVC [integer]
Min : 0
Mean : 0.2
Max : 1
0:15747(83.3%)
1:3156(16.7%)
 0 (0.0%)
28 CoMorbidite_Non.Cardio.Vasculaire_BPCO [integer]
Min : 0
Mean : 0.3
Max : 1
0:14027(74.2%)
1:4876(25.8%)
 0 (0.0%)
29 CoMorbidite_CardioVasculaire__Coro [integer]
Min : 0
Mean : 0.1
Max : 1
0:16340(86.4%)
1:2563(13.6%)
 0 (0.0%)
30 CoMorbidite_CardioVasculaire__Defib [integer]
Min : 0
Mean : 0
Max : 1
0:18750(99.2%)
1:153(0.8%)
 0 (0.0%)
31 CoMorbidite_Non.Cardio.Vasculaire_Depression [integer]
Min : 0
Mean : 0
Max : 1
0:18048(95.5%)
1:855(4.5%)
 0 (0.0%)
32 CoMorbidite_CardioVasculaire__Diabete [integer]
Min : 0
Mean : 0.2
Max : 1
0:14348(75.9%)
1:4555(24.1%)
 0 (0.0%)
33 CoMorbidite_CardioVasculaire__Dyslip [integer]
Min : 0
Mean : 0.3
Max : 1
0:13689(72.4%)
1:5214(27.6%)
 0 (0.0%)
34 CoMorbidite_CardioVasculaire__Embol [integer]
Min : 0
Mean : 0.2
Max : 1
0:15661(82.8%)
1:3242(17.2%)
 0 (0.0%)
35 CoMorbidite_CardioVasculaire__FA [integer]
Min : 0
Mean : 0.2
Max : 1
0:14474(76.6%)
1:4429(23.4%)
 0 (0.0%)
36 CoMorbidite_CardioVasculaire__Fumeur [integer]
Min : 0
Mean : 0.2
Max : 1
0:15880(84.0%)
1:3023(16.0%)
 0 (0.0%)
37 Trt_Furosemide [integer]
Min : 0
Mean : 0.4
Max : 1
0:11827(62.6%)
1:7076(37.4%)
 0 (0.0%)
38 CoMorbidite_CardioVasculaire__HTA [integer]
Min : 0
Mean : 0.5
Max : 1
0:9037(47.8%)
1:9866(52.2%)
 0 (0.0%)
39 CoMorbidite_Non.Cardio.Vasculaire_IRC [integer]
Min : 0
Mean : 0.2
Max : 1
0:15469(81.8%)
1:3434(18.2%)
 0 (0.0%)
40 CoMorbidite_CardioVasculaire__Obesite [integer]
Min : 0
Mean : 0
Max : 1
0:17960(95.0%)
1:943(5.0%)
 0 (0.0%)
41 CoMorbidite_CardioVasculaire__Pacemaker [integer]
Min : 0
Mean : 0.1
Max : 1
0:17447(92.3%)
1:1456(7.7%)
 0 (0.0%)
42 CoMorbidite_CardioVasculaire__Resync [integer]
Min : 0
Mean : 0
Max : 1
0:18875(99.9%)
1:28(0.1%)
 0 (0.0%)
43 CoMorbidite_CardioVasculaire__SCAST. [integer]
Min : 0
Mean : 0.1
Max : 1
0:17408(92.1%)
1:1495(7.9%)
 0 (0.0%)
44 CoMorbidite_CardioVasculaire__Valvulo [integer]
Min : 0
Mean : 0
Max : 1
0:18478(97.8%)
1:425(2.2%)
 0 (0.0%)
45 Cardiopathies [integer]
Min : 0
Mean : 0.3
Max : 1
0:13894(73.5%)
1:5009(26.5%)
 0 (0.0%)
46 ICC [integer]
Min : 0
Mean : 0.4
Max : 1
0:11582(61.3%)
1:7321(38.7%)
 0 (0.0%)
47 Glucose_SI [numeric]
Mean (sd) : 7.9 (3.8)
min ≤ med ≤ max:
0.2 ≤ 6.7 ≤ 57.1
IQR (CV) : 3 (0.5)
 755 distinct values 8197 (43.4%)
48 CL [numeric]
Mean (sd) : 101.6 (5.8)
min ≤ med ≤ max:
64 ≤ 102 ≤ 141
IQR (CV) : 6.5 (0.1)
 391 distinct values 4407 (23.3%)
49 TGO_ASAT_1 [numeric]
Mean (sd) : 76.6 (391.6)
min ≤ med ≤ max:
6 ≤ 29 ≤ 12966.9
IQR (CV) : 26.2 (5.1)
 1287 distinct values 13758 (72.8%)
50 TGP_ALAT [numeric]
Mean (sd) : 51.4 (227.8)
min ≤ med ≤ max:
1 ≤ 21 ≤ 7276
IQR (CV) : 22 (4.4)
 1206 distinct values 13782 (72.9%)
51 Quick_a [numeric]
Mean (sd) : 79.3 (14.5)
min ≤ med ≤ max:
10 ≤ 81 ≤ 100
IQR (CV) : 19 (0.2)
 362 distinct values 14546 (77.0%)
52 Hemoglobine [numeric]
Mean (sd) : 12.4 (2.2)
min ≤ med ≤ max:
3.6 ≤ 12.6 ≤ 21.4
IQR (CV) : 2.8 (0.2)
 156 distinct values 4931 (26.1%)
53 Globules_rouges [numeric]
Mean (sd) : 4.2 (0.7)
min ≤ med ≤ max:
1.1 ≤ 4.2 ≤ 9.6
IQR (CV) : 1 (0.2)
 517 distinct values 4931 (26.1%)
54 CVGR [numeric]
Mean (sd) : 15.3 (2.5)
min ≤ med ≤ max:
10.8 ≤ 14.7 ≤ 41.3
IQR (CV) : 2.7 (0.2)
 188 distinct values 4939 (26.1%)
55 Globules_blancs [numeric]
Mean (sd) : 11.3 (8.4)
min ≤ med ≤ max:
0 ≤ 9.9 ≤ 460.8
IQR (CV) : 6 (0.7)
 2075 distinct values 4932 (26.1%)
56 P_neutrophiles [numeric]
Mean (sd) : 8.8 (5.3)
min ≤ med ≤ max:
0 ≤ 7.5 ≤ 107.6
IQR (CV) : 5.7 (0.6)
 2254 distinct values 4973 (26.3%)
57 Lymphocytes [numeric]
Mean (sd) : 1.5 (5.2)
min ≤ med ≤ max:
0 ≤ 1.1 ≤ 460.8
IQR (CV) : 0.9 (3.5)
 587 distinct values 4973 (26.3%)
58 Monocytes [numeric]
Mean (sd) : 0.8 (1)
min ≤ med ≤ max:
0 ≤ 0.7 ≤ 51.2
IQR (CV) : 0.5 (1.2)
 349 distinct values 4973 (26.3%)
59 Plaquettes [numeric]
Mean (sd) : 252.8 (107.2)
min ≤ med ≤ max:
4 ≤ 237 ≤ 1533
IQR (CV) : 115 (0.4)
 684 distinct values 5142 (27.2%)
60 Prot_P [numeric]
Mean (sd) : 70 (7.3)
min ≤ med ≤ max:
17.5 ≤ 70 ≤ 114.5
IQR (CV) : 8.8 (0.1)
 446 distinct values 10662 (56.4%)
61 Proteines [numeric]
Mean (sd) : 71.5 (7.3)
min ≤ med ≤ max:
9 ≤ 72 ≤ 117
IQR (CV) : 9 (0.1)
 68 distinct values 11166 (59.1%)
62 Bili_Tot_SI [numeric]
Mean (sd) : 14.4 (18.7)
min ≤ med ≤ max:
1 ≤ 10.3 ≤ 567.2
IQR (CV) : 8.6 (1.3)
 622 distinct values 14122 (74.7%)
63 BNP [numeric]
Mean (sd) : 665.1 (896.4)
min ≤ med ≤ max:
3.7 ≤ 362 ≤ 9974
IQR (CV) : 681.5 (1.3)
 3959 distinct values 12688 (67.1%)
64 Ca_ion [numeric]
Mean (sd) : 1.2 (0.1)
min ≤ med ≤ max:
0.4 ≤ 1.1 ≤ 2.4
IQR (CV) : 0.1 (0.1)
 89 distinct values 14475 (76.6%)
65 Creat_SI [numeric]
Mean (sd) : 105.7 (84.5)
min ≤ med ≤ max:
4 ≤ 85 ≤ 2746.2
IQR (CV) : 54.9 (0.8)
 809 distinct values 4388 (23.2%)
66 CRP [numeric]
Mean (sd) : 79.8 (93.8)
min ≤ med ≤ max:
0.1 ≤ 41.4 ≤ 736.7
IQR (CV) : 105.5 (1.2)
 7420 distinct values 8035 (42.5%)
67 Glucose [numeric]
Mean (sd) : 1.4 (0.6)
min ≤ med ≤ max:
0.1 ≤ 1.2 ≤ 10.3
IQR (CV) : 0.5 (0.4)
 442 distinct values 4545 (24.0%)
68 HCO3_reels [numeric]
Mean (sd) : 26.3 (5.9)
min ≤ med ≤ max:
1 ≤ 25.9 ≤ 65.8
IQR (CV) : 6.3 (0.2)
 484 distinct values 2042 (10.8%)
69 Hematocrite [numeric]
Mean (sd) : 38.4 (6.6)
min ≤ med ≤ max:
11.6 ≤ 38.8 ≤ 66
IQR (CV) : 8.3 (0.2)
 448 distinct values 4170 (22.1%)
70 K [numeric]
Mean (sd) : 4.2 (0.7)
min ≤ med ≤ max:
1.2 ≤ 4.2 ≤ 20.9
IQR (CV) : 0.8 (0.2)
 433 distinct values 3228 (17.1%)
71 LACT [numeric]
Mean (sd) : 1.5 (1.5)
min ≤ med ≤ max:
0.1 ≤ 1.1 ≤ 22
IQR (CV) : 0.9 (1)
 145 distinct values 5262 (27.8%)
72 NA. [numeric]
Mean (sd) : 137 (5.4)
min ≤ med ≤ max:
101 ≤ 137 ≤ 177
IQR (CV) : 5.6 (0)
 348 distinct values 3212 (17.0%)
73 PCO2_corrigee [numeric]
Mean (sd) : 45.2 (16.5)
min ≤ med ≤ max:
6 ≤ 41 ≤ 176
IQR (CV) : 16 (0.4)
 728 distinct values 5009 (26.5%)
74 PH_corrige [numeric]
Mean (sd) : 7.4 (0.1)
min ≤ med ≤ max:
6.6 ≤ 7.4 ≤ 7.8
IQR (CV) : 0.1 (0)
 92 distinct values 4989 (26.4%)
75 PO2_corrige [numeric]
Mean (sd) : 77.9 (38.6)
min ≤ med ≤ max:
10 ≤ 69 ≤ 477
IQR (CV) : 28 (0.5)
 946 distinct values 5087 (26.9%)
76 Uree [numeric]
Mean (sd) : 0.6 (0.4)
min ≤ med ≤ max:
0 ≤ 0.5 ≤ 5.5
IQR (CV) : 0.4 (0.7)
 297 distinct values 4395 (23.3%)
77 URG_Diag [character]
1. Asthma
2. Autres
3. COPD
4. Embolie pulmonaire
5. ICA
6. Resp. infections
381(2.0%)
7757(41.0%)
247(1.3%)
572(3.0%)
3427(18.1%)
6519(34.5%)
 0 (0.0%)
78 PMSI_Diag [character]
1. Asthma
2. Autres
3. COPD
4. Embolie pulmonaire
5. ICA
6. Resp. infections
453(2.4%)
4405(23.3%)
2477(13.1%)
678(3.6%)
5305(28.1%)
5585(29.5%)
 0 (0.0%)
79 accord_Diag [integer]
Min : 0
Mean : 0.6
Max : 1
0:7425(39.3%)
1:11478(60.7%)
 0 (0.0%)
80 t2d_d [integer]
Mean (sd) : 914.5 (945.6)
min ≤ med ≤ max:
0 ≤ 599 ≤ 4213
IQR (CV) : 1355 (1)
 3329 distinct values 0 (0.0%)
81 DCintra_ddp [integer]
Min : 0
Mean : 0.1
Max : 1
0:16549(87.5%)
1:2354(12.5%)
 0 (0.0%)
82 DCextra_ddp [integer]
Min : 0
Mean : 0.5
Max : 1
0:10052(53.2%)
1:8851(46.8%)
 0 (0.0%)
83 DC_ddp [integer]
Min : 0
Mean : 0.6
Max : 1
0:7698(40.7%)
1:11205(59.3%)
 0 (0.0%)

Generated by summarytools 1.0.0 (R version 4.1.1)
2022-08-30

Nettoyage des percentils (20 variables concernées)

Visualisation des distributions du dataset Paradise après nettoyage percentiles (1% de part et d’autre)

Data Frame Summary

test.df_clean

Dimensions: 18903 x 70
Duplicates: 68
No Variable Stats / Values Freqs (% of Valid) Graph Missing
1 sexe [factor]
1. F
2. M
9812(51.9%)
9091(48.1%)
 0 (0.0%)
2 age [integer]
Mean (sd) : 73.4 (17.2)
min ≤ med ≤ max:
18 ≤ 78 ≤ 107
IQR (CV) : 22 (0.2)
 90 distinct values 0 (0.0%)
3 Trt_Ara2 [factor]
1. 0
2. 1
15450(81.7%)
3453(18.3%)
 0 (0.0%)
4 Trt_AntiAgr [factor]
1. 0
2. 1
12285(65.0%)
6618(35.0%)
 0 (0.0%)
5 Trt_AntiCoag [factor]
1. 0
2. 1
17843(94.4%)
1060(5.6%)
 0 (0.0%)
6 tt6h_broncho_beta2 [factor] 1. 0
18903(100.0%)
 0 (0.0%)
7 Trt_Bumetanide [factor]
1. 0
2. 1
18736(99.1%)
167(0.9%)
 0 (0.0%)
8 Trt_BetaBloquants [factor]
1. 0
2. 1
13116(69.4%)
5787(30.6%)
 0 (0.0%)
9 tt6h_diur [factor] 1. 0
18903(100.0%)
 0 (0.0%)
10 Trt_IEC [factor]
1. 0
2. 1
14643(77.5%)
4260(22.5%)
 0 (0.0%)
11 Trt_Insuline [factor]
1. 0
2. 1
16669(88.2%)
2234(11.8%)
 0 (0.0%)
12 OEDEME [factor]
1. 0
2. 1
14253(75.4%)
4650(24.6%)
 0 (0.0%)
13 tt6h_broncho_parasymp [factor] 1. 0
18903(100.0%)
 0 (0.0%)
14 RONCHI [factor]
1. 0
2. 1
16583(87.7%)
2320(12.3%)
 0 (0.0%)
15 RALES [factor]
1. 0
2. 1
12376(65.5%)
6527(34.5%)
 0 (0.0%)
16 SIBILANTS [factor]
1. 0
2. 1
15934(84.3%)
2969(15.7%)
 0 (0.0%)
17 Trt_Statine [factor]
1. 0
2. 1
13991(74.0%)
4912(26.0%)
 0 (0.0%)
18 TURGJUG [factor]
1. 0
2. 1
18841(99.7%)
62(0.3%)
 0 (0.0%)
19 tt6h_vni [factor]
1. 0
2. 1
15558(82.3%)
3345(17.7%)
 0 (0.0%)
20 tth6_vasodil [factor]
1. 0
2. 1
17487(92.5%)
1416(7.5%)
 0 (0.0%)
21 CoMorbidite_Non.Cardio.Vasculaire_Anemie [factor]
1. 0
2. 1
17331(91.7%)
1572(8.3%)
 0 (0.0%)
22 CoMorbidite_CardioVasculaire__Angio [factor]
1. 0
2. 1
15792(83.5%)
3111(16.5%)
 0 (0.0%)
23 CoMorbidite_CardioVasculaire__Arterite [factor]
1. 0
2. 1
16824(89.0%)
2079(11.0%)
 0 (0.0%)
24 CoMorbidite_Non.Cardio.Vasculaire_Asthme [factor]
1. 0
2. 1
17149(90.7%)
1754(9.3%)
 0 (0.0%)
25 CoMorbidite_CardioVasculaire__AVC [factor]
1. 0
2. 1
15747(83.3%)
3156(16.7%)
 0 (0.0%)
26 CoMorbidite_Non.Cardio.Vasculaire_BPCO [factor]
1. 0
2. 1
14027(74.2%)
4876(25.8%)
 0 (0.0%)
27 CoMorbidite_CardioVasculaire__Coro [factor]
1. 0
2. 1
16340(86.4%)
2563(13.6%)
 0 (0.0%)
28 CoMorbidite_CardioVasculaire__Defib [factor]
1. 0
2. 1
18750(99.2%)
153(0.8%)
 0 (0.0%)
29 CoMorbidite_Non.Cardio.Vasculaire_Depression [factor]
1. 0
2. 1
18048(95.5%)
855(4.5%)
 0 (0.0%)
30 CoMorbidite_CardioVasculaire__Diabete [factor]
1. 0
2. 1
14348(75.9%)
4555(24.1%)
 0 (0.0%)
31 CoMorbidite_CardioVasculaire__Dyslip [factor]
1. 0
2. 1
13689(72.4%)
5214(27.6%)
 0 (0.0%)
32 CoMorbidite_CardioVasculaire__Embol [factor]
1. 0
2. 1
15661(82.8%)
3242(17.2%)
 0 (0.0%)
33 CoMorbidite_CardioVasculaire__FA [factor]
1. 0
2. 1
14474(76.6%)
4429(23.4%)
 0 (0.0%)
34 CoMorbidite_CardioVasculaire__Fumeur [factor]
1. 0
2. 1
15880(84.0%)
3023(16.0%)
 0 (0.0%)
35 Trt_Furosemide [factor]
1. 0
2. 1
11827(62.6%)
7076(37.4%)
 0 (0.0%)
36 CoMorbidite_CardioVasculaire__HTA [factor]
1. 0
2. 1
9037(47.8%)
9866(52.2%)
 0 (0.0%)
37 CoMorbidite_Non.Cardio.Vasculaire_IRC [factor]
1. 0
2. 1
15469(81.8%)
3434(18.2%)
 0 (0.0%)
38 CoMorbidite_CardioVasculaire__Obesite [factor]
1. 0
2. 1
17960(95.0%)
943(5.0%)
 0 (0.0%)
39 CoMorbidite_CardioVasculaire__Pacemaker [factor]
1. 0
2. 1
17447(92.3%)
1456(7.7%)
 0 (0.0%)
40 CoMorbidite_CardioVasculaire__Resync [factor]
1. 0
2. 1
18875(99.9%)
28(0.1%)
 0 (0.0%)
41 CoMorbidite_CardioVasculaire__SCAST. [factor]
1. 0
2. 1
17408(92.1%)
1495(7.9%)
 0 (0.0%)
42 CoMorbidite_CardioVasculaire__Valvulo [factor]
1. 0
2. 1
18478(97.8%)
425(2.2%)
 0 (0.0%)
43 Cardiopathies [factor]
1. 0
2. 1
13894(73.5%)
5009(26.5%)
 0 (0.0%)
44 ICC [factor]
1. 0
2. 1
11582(61.3%)
7321(38.7%)
 0 (0.0%)
45 CL [numeric]
Mean (sd) : 101.5 (5.3)
min ≤ med ≤ max:
85 ≤ 102 ≤ 116
IQR (CV) : 6.5 (0.1)
 288 distinct values 4407 (23.3%)
46 Hemoglobine [numeric]
Mean (sd) : 12.4 (2.2)
min ≤ med ≤ max:
6.5 ≤ 12.6 ≤ 17.1
IQR (CV) : 2.8 (0.2)
 107 distinct values 4931 (26.1%)
47 Globules_rouges [numeric]
Mean (sd) : 4.2 (0.7)
min ≤ med ≤ max:
1.1 ≤ 4.2 ≤ 5.9
IQR (CV) : 1 (0.2)
 438 distinct values 4931 (26.1%)
48 CVGR [numeric]
Mean (sd) : 15.3 (2.3)
min ≤ med ≤ max:
12 ≤ 14.7 ≤ 23.9
IQR (CV) : 2.7 (0.2)
 121 distinct values 4939 (26.1%)
49 Globules_blancs [numeric]
Mean (sd) : 11.1 (5.3)
min ≤ med ≤ max:
0 ≤ 9.9 ≤ 32.1
IQR (CV) : 6 (0.5)
 1952 distinct values 4932 (26.1%)
50 P_neutrophiles [numeric]
Mean (sd) : 8.7 (4.9)
min ≤ med ≤ max:
0 ≤ 7.5 ≤ 26.8
IQR (CV) : 5.7 (0.6)
 2119 distinct values 4973 (26.3%)
51 Lymphocytes [numeric]
Mean (sd) : 1.3 (0.9)
min ≤ med ≤ max:
0 ≤ 1.1 ≤ 5.1
IQR (CV) : 0.9 (0.6)
 467 distinct values 4973 (26.3%)
52 Monocytes [numeric]
Mean (sd) : 0.8 (0.4)
min ≤ med ≤ max:
0 ≤ 0.7 ≤ 2.4
IQR (CV) : 0.5 (0.5)
 241 distinct values 4973 (26.3%)
53 Plaquettes [numeric]
Mean (sd) : 251.5 (100.5)
min ≤ med ≤ max:
4 ≤ 237 ≤ 605.4
IQR (CV) : 115 (0.4)
 580 distinct values 5142 (27.2%)
54 Creat_SI [numeric]
Mean (sd) : 103.3 (64.5)
min ≤ med ≤ max:
4 ≤ 85 ≤ 434.8
IQR (CV) : 54.9 (0.6)
 676 distinct values 4388 (23.2%)
55 Glucose [numeric]
Mean (sd) : 1.3 (0.5)
min ≤ med ≤ max:
0.7 ≤ 1.2 ≤ 3.7
IQR (CV) : 0.5 (0.4)
 301 distinct values 4545 (24.0%)
56 HCO3_reels [numeric]
Mean (sd) : 26.3 (5.7)
min ≤ med ≤ max:
12.1 ≤ 25.9 ≤ 45.2
IQR (CV) : 6.3 (0.2)
 329 distinct values 2042 (10.8%)
57 Hematocrite [numeric]
Mean (sd) : 38.4 (6.4)
min ≤ med ≤ max:
20.7 ≤ 38.8 ≤ 53.9
IQR (CV) : 8.3 (0.2)
 332 distinct values 4170 (22.1%)
58 K [numeric]
Mean (sd) : 4.2 (0.7)
min ≤ med ≤ max:
2.8 ≤ 4.2 ≤ 6.5
IQR (CV) : 0.8 (0.2)
 341 distinct values 3228 (17.1%)
59 LACT [numeric]
Mean (sd) : 1.5 (1.3)
min ≤ med ≤ max:
0.1 ≤ 1.1 ≤ 8
IQR (CV) : 0.9 (0.8)
 80 distinct values 5262 (27.8%)
60 NA. [numeric]
Mean (sd) : 137 (4.9)
min ≤ med ≤ max:
122 ≤ 137 ≤ 152
IQR (CV) : 5.6 (0)
 269 distinct values 3212 (17.0%)
61 PCO2_corrigee [numeric]
Mean (sd) : 45.1 (15.8)
min ≤ med ≤ max:
21 ≤ 41 ≤ 105
IQR (CV) : 16 (0.4)
 654 distinct values 5009 (26.5%)
62 PH_corrige [numeric]
Mean (sd) : 7.4 (0.1)
min ≤ med ≤ max:
6.6 ≤ 7.4 ≤ 7.8
IQR (CV) : 0.1 (0)
 92 distinct values 4989 (26.4%)
63 PO2_corrige [numeric]
Mean (sd) : 77.2 (34.3)
min ≤ med ≤ max:
10 ≤ 69 ≤ 240
IQR (CV) : 28 (0.4)
 857 distinct values 5087 (26.9%)
64 Uree [numeric]
Mean (sd) : 0.6 (0.4)
min ≤ med ≤ max:
0 ≤ 0.5 ≤ 2.2
IQR (CV) : 0.4 (0.6)
 210 distinct values 4395 (23.3%)
65 URG_Diag [factor]
1. Asthma
2. Autres
3. COPD
4. Embolie pulmonaire
5. ICA
6. Resp. infections
381(2.0%)
7757(41.0%)
247(1.3%)
572(3.0%)
3427(18.1%)
6519(34.5%)
 0 (0.0%)
66 PMSI_Diag [factor]
1. Asthma
2. Autres
3. COPD
4. Embolie pulmonaire
5. ICA
6. Resp. infections
453(2.4%)
4405(23.3%)
2477(13.1%)
678(3.6%)
5305(28.1%)
5585(29.5%)
 0 (0.0%)
67 accord_Diag [factor]
1. 0
2. 1
7425(39.3%)
11478(60.7%)
 0 (0.0%)
68 DCintra_ddp [factor]
1. 0
2. 1
16549(87.5%)
2354(12.5%)
 0 (0.0%)
69 DCextra_ddp [factor]
1. 0
2. 1
10052(53.2%)
8851(46.8%)
 0 (0.0%)
70 DC_ddp [factor]
1. 0
2. 1
7698(40.7%)
11205(59.3%)
 0 (0.0%)

Generated by summarytools 1.0.0 (R version 4.1.1)
2022-08-30

Clustering avec sélection de variable (intérvale 1 à 10)

Voici les variables les plus discriminantes

Clustering: Avec 5 clusters

Voici les variables les plus discriminantes

Probabilités estimées de classement pour notre clustering (nous voyons que les 7 clusters sont plustôt bien équlibré)

Résumé des probabilités de mauvaise classification

Table descriptive des variables en fonction des clusters

Characteristic Overall, N = 18,9031 1, N = 2,1311 2, N = 2,7751 3, N = 1,4331 4, N = 3,3091 5, N = 3,5021 6, N = 3,3811 7, N = 2,3721 p-value2
sexe <0.001
F 9,812 (52%) 889 (42%) 1,792 (65%) 655 (46%) 1,995 (60%) 1,794 (51%) 1,491 (44%) 1,196 (50%)
M 9,091 (48%) 1,242 (58%) 983 (35%) 778 (54%) 1,314 (40%) 1,708 (49%) 1,890 (56%) 1,176 (50%)
age 78 (64, 86) 57 (50, 64) 83 (76, 89) 34 (26, 43) 85 (78, 89) 74 (64, 83) 81 (74, 88) 82 (73, 88) <0.001
Trt_Ara2 <0.001
0 15,450 (82%) 2,020 (95%) 2,194 (79%) 1,427 (100%) 2,518 (76%) 2,831 (81%) 2,608 (77%) 1,852 (78%)
1 3,453 (18%) 111 (5.2%) 581 (21%) 6 (0.4%) 791 (24%) 671 (19%) 773 (23%) 520 (22%)
Trt_AntiAgr <0.001
0 12,285 (65%) 1,926 (90%) 1,724 (62%) 1,416 (99%) 2,122 (64%) 2,490 (71%) 1,167 (35%) 1,440 (61%)
1 6,618 (35%) 205 (9.6%) 1,051 (38%) 17 (1.2%) 1,187 (36%) 1,012 (29%) 2,214 (65%) 932 (39%)
Trt_AntiCoag <0.001
0 17,843 (94%) 2,110 (99%) 2,517 (91%) 1,426 (100%) 3,097 (94%) 3,366 (96%) 3,060 (91%) 2,267 (96%)
1 1,060 (5.6%) 21 (1.0%) 258 (9.3%) 7 (0.5%) 212 (6.4%) 136 (3.9%) 321 (9.5%) 105 (4.4%)
tt6h_broncho_beta2
0 18,903 (100%) 2,131 (100%) 2,775 (100%) 1,433 (100%) 3,309 (100%) 3,502 (100%) 3,381 (100%) 2,372 (100%)
Trt_Bumetanide <0.001
0 18,736 (99%) 2,127 (100%) 2,747 (99%) 1,433 (100%) 3,289 (99%) 3,494 (100%) 3,299 (98%) 2,347 (99%)
1 167 (0.9%) 4 (0.2%) 28 (1.0%) 0 (0%) 20 (0.6%) 8 (0.2%) 82 (2.4%) 25 (1.1%)
Trt_BetaBloquants <0.001
0 13,116 (69%) 2,031 (95%) 1,801 (65%) 1,402 (98%) 2,447 (74%) 2,772 (79%) 1,101 (33%) 1,562 (66%)
1 5,787 (31%) 100 (4.7%) 974 (35%) 31 (2.2%) 862 (26%) 730 (21%) 2,280 (67%) 810 (34%)
tt6h_diur
0 18,903 (100%) 2,131 (100%) 2,775 (100%) 1,433 (100%) 3,309 (100%) 3,502 (100%) 3,381 (100%) 2,372 (100%)
Trt_IEC <0.001
0 14,643 (77%) 2,002 (94%) 1,933 (70%) 1,411 (98%) 2,779 (84%) 3,003 (86%) 1,660 (49%) 1,855 (78%)
1 4,260 (23%) 129 (6.1%) 842 (30%) 22 (1.5%) 530 (16%) 499 (14%) 1,721 (51%) 517 (22%)
Trt_Insuline <0.001
0 16,669 (88%) 2,072 (97%) 2,400 (86%) 1,430 (100%) 3,023 (91%) 3,392 (97%) 2,336 (69%) 2,016 (85%)
1 2,234 (12%) 59 (2.8%) 375 (14%) 3 (0.2%) 286 (8.6%) 110 (3.1%) 1,045 (31%) 356 (15%)
OEDEME <0.001
0 14,253 (75%) 1,765 (83%) 1,789 (64%) 1,326 (93%) 2,637 (80%) 2,904 (83%) 2,080 (62%) 1,752 (74%)
1 4,650 (25%) 366 (17%) 986 (36%) 107 (7.5%) 672 (20%) 598 (17%) 1,301 (38%) 620 (26%)
tt6h_broncho_parasymp
0 18,903 (100%) 2,131 (100%) 2,775 (100%) 1,433 (100%) 3,309 (100%) 3,502 (100%) 3,381 (100%) 2,372 (100%)
RONCHI <0.001
0 16,583 (88%) 1,845 (87%) 2,364 (85%) 1,326 (93%) 2,852 (86%) 3,072 (88%) 3,055 (90%) 2,069 (87%)
1 2,320 (12%) 286 (13%) 411 (15%) 107 (7.5%) 457 (14%) 430 (12%) 326 (9.6%) 303 (13%)
RALES <0.001
0 12,376 (65%) 1,706 (80%) 1,630 (59%) 1,301 (91%) 1,920 (58%) 2,595 (74%) 1,849 (55%) 1,375 (58%)
1 6,527 (35%) 425 (20%) 1,145 (41%) 132 (9.2%) 1,389 (42%) 907 (26%) 1,532 (45%) 997 (42%)
SIBILANTS <0.001
0 15,934 (84%) 1,786 (84%) 2,132 (77%) 1,244 (87%) 2,907 (88%) 2,909 (83%) 2,868 (85%) 2,088 (88%)
1 2,969 (16%) 345 (16%) 643 (23%) 189 (13%) 402 (12%) 593 (17%) 513 (15%) 284 (12%)
Trt_Statine <0.001
0 13,991 (74%) 2,008 (94%) 2,110 (76%) 1,423 (99%) 2,678 (81%) 2,702 (77%) 1,344 (40%) 1,726 (73%)
1 4,912 (26%) 123 (5.8%) 665 (24%) 10 (0.7%) 631 (19%) 800 (23%) 2,037 (60%) 646 (27%)
TURGJUG
0 18,841 (100%) 2,127 (100%) 2,760 (99%) 1,433 (100%) 3,304 (100%) 3,490 (100%) 3,363 (99%) 2,364 (100%)
1 62 (0.3%) 4 (0.2%) 15 (0.5%) 0 (0%) 5 (0.2%) 12 (0.3%) 18 (0.5%) 8 (0.3%)
tt6h_vni <0.001
0 15,558 (82%) 1,653 (78%) 1,297 (47%) 1,407 (98%) 3,076 (93%) 3,402 (97%) 3,065 (91%) 1,658 (70%)
1 3,345 (18%) 478 (22%) 1,478 (53%) 26 (1.8%) 233 (7.0%) 100 (2.9%) 316 (9.3%) 714 (30%)
tth6_vasodil <0.001
0 17,487 (93%) 2,084 (98%) 2,381 (86%) 1,426 (100%) 3,110 (94%) 3,415 (98%) 3,020 (89%) 2,051 (86%)
1 1,416 (7.5%) 47 (2.2%) 394 (14%) 7 (0.5%) 199 (6.0%) 87 (2.5%) 361 (11%) 321 (14%)
CoMorbidite_Non.Cardio.Vasculaire_Anemie <0.001
0 17,331 (92%) 1,915 (90%) 2,608 (94%) 1,399 (98%) 3,194 (97%) 3,446 (98%) 2,851 (84%) 1,918 (81%)
1 1,572 (8.3%) 216 (10%) 167 (6.0%) 34 (2.4%) 115 (3.5%) 56 (1.6%) 530 (16%) 454 (19%)
CoMorbidite_CardioVasculaire__Angio <0.001
0 15,792 (84%) 2,042 (96%) 2,434 (88%) 1,328 (93%) 3,039 (92%) 3,099 (88%) 1,886 (56%) 1,964 (83%)
1 3,111 (16%) 89 (4.2%) 341 (12%) 105 (7.3%) 270 (8.2%) 403 (12%) 1,495 (44%) 408 (17%)
CoMorbidite_CardioVasculaire__Arterite <0.001
0 16,824 (89%) 2,019 (95%) 2,467 (89%) 1,390 (97%) 3,032 (92%) 3,185 (91%) 2,648 (78%) 2,083 (88%)
1 2,079 (11%) 112 (5.3%) 308 (11%) 43 (3.0%) 277 (8.4%) 317 (9.1%) 733 (22%) 289 (12%)
CoMorbidite_Non.Cardio.Vasculaire_Asthme <0.001
0 17,149 (91%) 1,890 (89%) 2,430 (88%) 1,132 (79%) 3,117 (94%) 3,114 (89%) 3,214 (95%) 2,252 (95%)
1 1,754 (9.3%) 241 (11%) 345 (12%) 301 (21%) 192 (5.8%) 388 (11%) 167 (4.9%) 120 (5.1%)
CoMorbidite_CardioVasculaire__AVC <0.001
0 15,747 (83%) 2,056 (96%) 2,324 (84%) 1,409 (98%) 2,747 (83%) 3,079 (88%) 2,193 (65%) 1,939 (82%)
1 3,156 (17%) 75 (3.5%) 451 (16%) 24 (1.7%) 562 (17%) 423 (12%) 1,188 (35%) 433 (18%)
CoMorbidite_Non.Cardio.Vasculaire_BPCO <0.001
0 14,027 (74%) 1,432 (67%) 1,567 (56%) 1,350 (94%) 2,664 (81%) 2,474 (71%) 2,604 (77%) 1,936 (82%)
1 4,876 (26%) 699 (33%) 1,208 (44%) 83 (5.8%) 645 (19%) 1,028 (29%) 777 (23%) 436 (18%)
CoMorbidite_CardioVasculaire__Coro <0.001
0 16,340 (86%) 2,095 (98%) 2,410 (87%) 1,420 (99%) 3,076 (93%) 3,301 (94%) 2,002 (59%) 2,036 (86%)
1 2,563 (14%) 36 (1.7%) 365 (13%) 13 (0.9%) 233 (7.0%) 201 (5.7%) 1,379 (41%) 336 (14%)
CoMorbidite_CardioVasculaire__Defib <0.001
0 18,750 (99%) 2,130 (100%) 2,770 (100%) 1,432 (100%) 3,305 (100%) 3,495 (100%) 3,265 (97%) 2,353 (99%)
1 153 (0.8%) 1 (<0.1%) 5 (0.2%) 1 (<0.1%) 4 (0.1%) 7 (0.2%) 116 (3.4%) 19 (0.8%)
CoMorbidite_Non.Cardio.Vasculaire_Depression <0.001
0 18,048 (95%) 2,025 (95%) 2,627 (95%) 1,395 (97%) 3,120 (94%) 3,362 (96%) 3,261 (96%) 2,258 (95%)
1 855 (4.5%) 106 (5.0%) 148 (5.3%) 38 (2.7%) 189 (5.7%) 140 (4.0%) 120 (3.5%) 114 (4.8%)
CoMorbidite_CardioVasculaire__Diabete <0.001
0 14,348 (76%) 1,909 (90%) 2,036 (73%) 1,410 (98%) 2,586 (78%) 3,076 (88%) 1,769 (52%) 1,562 (66%)
1 4,555 (24%) 222 (10%) 739 (27%) 23 (1.6%) 723 (22%) 426 (12%) 1,612 (48%) 810 (34%)
CoMorbidite_CardioVasculaire__Dyslip <0.001
0 13,689 (72%) 1,971 (92%) 2,021 (73%) 1,397 (97%) 2,562 (77%) 2,610 (75%) 1,458 (43%) 1,670 (70%)
1 5,214 (28%) 160 (7.5%) 754 (27%) 36 (2.5%) 747 (23%) 892 (25%) 1,923 (57%) 702 (30%)
CoMorbidite_CardioVasculaire__Embol <0.001
0 15,661 (83%) 1,899 (89%) 2,255 (81%) 1,222 (85%) 2,722 (82%) 2,818 (80%) 2,755 (81%) 1,990 (84%)
1 3,242 (17%) 232 (11%) 520 (19%) 211 (15%) 587 (18%) 684 (20%) 626 (19%) 382 (16%)
CoMorbidite_CardioVasculaire__FA <0.001
0 14,474 (77%) 2,074 (97%) 1,781 (64%) 1,424 (99%) 2,408 (73%) 2,987 (85%) 2,086 (62%) 1,714 (72%)
1 4,429 (23%) 57 (2.7%) 994 (36%) 9 (0.6%) 901 (27%) 515 (15%) 1,295 (38%) 658 (28%)
CoMorbidite_CardioVasculaire__Fumeur <0.001
0 15,880 (84%) 1,517 (71%) 2,306 (83%) 1,145 (80%) 3,054 (92%) 2,832 (81%) 2,875 (85%) 2,151 (91%)
1 3,023 (16%) 614 (29%) 469 (17%) 288 (20%) 255 (7.7%) 670 (19%) 506 (15%) 221 (9.3%)
Trt_Furosemide <0.001
0 11,827 (63%) 1,980 (93%) 1,005 (36%) 1,428 (100%) 2,225 (67%) 2,919 (83%) 926 (27%) 1,344 (57%)
1 7,076 (37%) 151 (7.1%) 1,770 (64%) 5 (0.3%) 1,084 (33%) 583 (17%) 2,455 (73%) 1,028 (43%)
CoMorbidite_CardioVasculaire__HTA <0.001
0 9,037 (48%) 1,759 (83%) 924 (33%) 1,368 (95%) 1,253 (38%) 1,888 (54%) 963 (28%) 882 (37%)
1 9,866 (52%) 372 (17%) 1,851 (67%) 65 (4.5%) 2,056 (62%) 1,614 (46%) 2,418 (72%) 1,490 (63%)
CoMorbidite_Non.Cardio.Vasculaire_IRC <0.001
0 15,469 (82%) 1,984 (93%) 2,135 (77%) 1,418 (99%) 2,907 (88%) 3,301 (94%) 2,236 (66%) 1,488 (63%)
1 3,434 (18%) 147 (6.9%) 640 (23%) 15 (1.0%) 402 (12%) 201 (5.7%) 1,145 (34%) 884 (37%)
CoMorbidite_CardioVasculaire__Obesite <0.001
0 17,960 (95%) 2,015 (95%) 2,497 (90%) 1,417 (99%) 3,217 (97%) 3,406 (97%) 3,130 (93%) 2,278 (96%)
1 943 (5.0%) 116 (5.4%) 278 (10%) 16 (1.1%) 92 (2.8%) 96 (2.7%) 251 (7.4%) 94 (4.0%)
CoMorbidite_CardioVasculaire__Pacemaker <0.001
0 17,447 (92%) 2,124 (100%) 2,514 (91%) 1,428 (100%) 3,044 (92%) 3,379 (96%) 2,820 (83%) 2,138 (90%)
1 1,456 (7.7%) 7 (0.3%) 261 (9.4%) 5 (0.3%) 265 (8.0%) 123 (3.5%) 561 (17%) 234 (9.9%)
CoMorbidite_CardioVasculaire__Resync
0 18,875 (100%) 2,131 (100%) 2,773 (100%) 1,432 (100%) 3,308 (100%) 3,502 (100%) 3,360 (99%) 2,369 (100%)
1 28 (0.1%) 0 (0%) 2 (<0.1%) 1 (<0.1%) 1 (<0.1%) 0 (0%) 21 (0.6%) 3 (0.1%)
CoMorbidite_CardioVasculaire__SCAST. <0.001
0 17,408 (92%) 2,108 (99%) 2,618 (94%) 1,417 (99%) 3,184 (96%) 3,327 (95%) 2,564 (76%) 2,190 (92%)
1 1,495 (7.9%) 23 (1.1%) 157 (5.7%) 16 (1.1%) 125 (3.8%) 175 (5.0%) 817 (24%) 182 (7.7%)
CoMorbidite_CardioVasculaire__Valvulo <0.001
0 18,478 (98%) 2,127 (100%) 2,704 (97%) 1,430 (100%) 3,242 (98%) 3,460 (99%) 3,207 (95%) 2,308 (97%)
1 425 (2.2%) 4 (0.2%) 71 (2.6%) 3 (0.2%) 67 (2.0%) 42 (1.2%) 174 (5.1%) 64 (2.7%)
Cardiopathies <0.001
0 13,894 (74%) 2,004 (94%) 2,120 (76%) 1,304 (91%) 2,817 (85%) 2,903 (83%) 1,050 (31%) 1,696 (72%)
1 5,009 (26%) 127 (6.0%) 655 (24%) 129 (9.0%) 492 (15%) 599 (17%) 2,331 (69%) 676 (28%)
ICC <0.001
0 11,582 (61%) 1,965 (92%) 1,044 (38%) 1,386 (97%) 2,284 (69%) 2,970 (85%) 662 (20%) 1,271 (54%)
1 7,321 (39%) 166 (7.8%) 1,731 (62%) 47 (3.3%) 1,025 (31%) 532 (15%) 2,719 (80%) 1,101 (46%)
CL 102.0 (98.5, 105.0) 99.0 (95.0, 103.0) 100.0 (96.0, 103.0) 104.0 (101.0, 105.7) 101.0 (97.9, 104.0) 102.1 (99.9, 105.0) 103.1 (100.7, 106.0) 103.0 (98.5, 107.0) <0.001
Hemoglobine 12.60 (11.10, 13.90) 12.60 (10.60, 14.60) 12.20 (11.10, 13.30) 14.00 (12.90, 15.00) 12.70 (11.50, 13.70) 13.50 (12.60, 14.50) 11.40 (10.00, 12.70) 11.10 (9.30, 13.00) <0.001
Globules_rouges 4.18 (3.70, 4.65) 4.21 (3.56, 4.86) 4.11 (3.72, 4.50) 4.66 (4.29, 5.01) 4.19 (3.80, 4.59) 4.45 (4.11, 4.79) 3.87 (3.41, 4.34) 3.72 (3.12, 4.34) <0.001
CVGR 14.70 (13.60, 16.30) 14.80 (13.50, 16.90) 15.30 (14.20, 16.90) 13.20 (12.50, 14.00) 14.60 (13.70, 15.85) 13.90 (13.20, 14.80) 15.70 (14.40, 17.50) 16.00 (14.60, 18.10) <0.001
Globules_blancs 9.9 (7.4, 13.4) 11.6 (7.9, 15.8) 8.2 (6.8, 10.0) 10.3 (7.9, 13.1) 14.6 (11.5, 18.0) 8.6 (7.0, 10.6) 8.3 (6.5, 10.4) 13.2 (9.0, 18.3) <0.001
P_neutrophiles 7.5 (5.2, 10.9) 9.2 (5.5, 13.0) 6.2 (4.7, 7.9) 7.1 (4.8, 10.2) 12.4 (9.3, 15.7) 6.2 (4.6, 8.1) 6.2 (4.6, 8.3) 10.1 (6.4, 14.9) <0.001
Lymphocytes 1.14 (0.74, 1.69) 1.23 (0.76, 1.91) 1.04 (0.72, 1.44) 1.85 (1.21, 2.51) 0.99 (0.63, 1.44) 1.35 (0.91, 1.87) 1.01 (0.70, 1.43) 1.05 (0.63, 1.85) <0.001
Monocytes 0.72 (0.51, 0.98) 0.79 (0.48, 1.13) 0.67 (0.51, 0.86) 0.71 (0.53, 0.95) 0.90 (0.62, 1.24) 0.67 (0.51, 0.86) 0.66 (0.49, 0.85) 0.79 (0.50, 1.19) <0.001
Plaquettes 237 (186, 301) 265 (184, 357) 228 (183, 284) 244 (203, 296) 263 (206, 338) 234 (189, 284) 216 (170, 271) 237 (168, 329) <0.001
Creat_SI 85 (65, 120) 63 (49, 77) 85 (65, 111) 65 (53, 78) 88 (69, 113) 73 (59, 87) 119 (90, 158) 163 (106, 241) <0.001
Glucose 1.18 (1.01, 1.48) 1.17 (0.99, 1.50) 1.16 (1.00, 1.41) 1.01 (0.91, 1.13) 1.32 (1.11, 1.67) 1.11 (1.00, 1.26) 1.24 (1.03, 1.67) 1.33 (1.06, 1.85) <0.001
HCO3_reels 25.9 (22.9, 29.2) 26.6 (23.2, 31.2) 32.3 (28.7, 36.7) 24.9 (22.9, 27.0) 25.2 (22.8, 27.8) 26.0 (24.0, 28.1) 24.9 (22.4, 27.7) 21.9 (17.9, 25.4) <0.001
Hematocrite 39 (34, 43) 39 (33, 45) 38 (35, 42) 42 (39, 45) 39 (35, 42) 41 (38, 44) 36 (32, 40) 35 (29, 41) <0.001
K 4.20 (3.80, 4.60) 4.10 (3.70, 4.42) 4.30 (3.90, 4.70) 3.90 (3.70, 4.18) 4.10 (3.70, 4.45) 4.05 (3.80, 4.32) 4.30 (4.00, 4.70) 4.50 (4.00, 5.20) <0.001
LACT 1.10 (0.80, 1.70) 1.30 (0.80, 2.10) 0.80 (0.60, 1.10) 1.10 (0.80, 1.60) 1.30 (0.90, 1.90) 0.90 (0.70, 1.20) 1.10 (0.80, 1.60) 2.30 (1.20, 4.10) <0.001
NA. 137.0 (134.4, 140.0) 135.0 (131.9, 138.0) 138.5 (135.9, 141.0) 137.0 (135.6, 139.0) 136.7 (133.7, 139.0) 137.3 (135.0, 139.3) 138.0 (135.8, 140.0) 137.0 (133.0, 140.0) <0.001
PCO2_corrigee 41 (35, 51) 45 (35, 60) 62 (53, 75) 37 (33, 41) 38 (34, 43) 39 (35, 44) 39 (34, 44) 39 (30, 52) <0.001
PH_corrige 7.41 (7.34, 7.45) 7.39 (7.31, 7.45) 7.33 (7.27, 7.38) 7.43 (7.40, 7.46) 7.43 (7.40, 7.47) 7.43 (7.40, 7.46) 7.42 (7.38, 7.45) 7.33 (7.24, 7.41) <0.001
PO2_corrige 69 (58, 86) 69 (57, 89) 78 (61, 105) 70 (59, 85) 68 (57, 83) 65 (57, 75) 67 (57, 81) 73 (58, 100) <0.001
Uree 0.49 (0.33, 0.72) 0.32 (0.24, 0.44) 0.53 (0.38, 0.71) 0.26 (0.20, 0.32) 0.54 (0.41, 0.72) 0.37 (0.29, 0.46) 0.68 (0.49, 0.95) 1.00 (0.65, 1.49) <0.001
URG_Diag <0.001
1 381 (2.0%) 62 (2.9%) 29 (1.0%) 174 (12%) 11 (0.3%) 81 (2.3%) 20 (0.6%) 4 (0.2%)
2 7,757 (41%) 1,155 (54%) 1,251 (45%) 776 (54%) 777 (23%) 1,639 (47%) 1,176 (35%) 983 (41%)
3 247 (1.3%) 53 (2.5%) 57 (2.1%) 9 (0.6%) 21 (0.6%) 74 (2.1%) 25 (0.7%) 8 (0.3%)
4 572 (3.0%) 55 (2.6%) 14 (0.5%) 118 (8.2%) 76 (2.3%) 233 (6.7%) 46 (1.4%) 30 (1.3%)
5 3,427 (18%) 83 (3.9%) 682 (25%) 17 (1.2%) 470 (14%) 282 (8.1%) 1,254 (37%) 639 (27%)
6 6,519 (34%) 723 (34%) 742 (27%) 339 (24%) 1,954 (59%) 1,193 (34%) 860 (25%) 708 (30%)
PMSI_Diag <0.001
Asthma 453 (2.4%) 59 (2.8%) 47 (1.7%) 190 (13%) 32 (1.0%) 89 (2.5%) 28 (0.8%) 8 (0.3%)
Autres 4,405 (23%) 673 (32%) 413 (15%) 697 (49%) 516 (16%) 1,021 (29%) 557 (16%) 528 (22%)
COPD 2,477 (13%) 497 (23%) 608 (22%) 55 (3.8%) 285 (8.6%) 581 (17%) 282 (8.3%) 169 (7.1%)
Embolie pulmonaire 678 (3.6%) 64 (3.0%) 20 (0.7%) 117 (8.2%) 114 (3.4%) 276 (7.9%) 54 (1.6%) 33 (1.4%)
ICA 5,305 (28%) 211 (9.9%) 1,063 (38%) 37 (2.6%) 756 (23%) 511 (15%) 1,770 (52%) 957 (40%)
Resp. infections 5,585 (30%) 627 (29%) 624 (22%) 337 (24%) 1,606 (49%) 1,024 (29%) 690 (20%) 677 (29%)
accord_Diag <0.001
0 7,425 (39%) 929 (44%) 1,500 (54%) 269 (19%) 1,162 (35%) 1,280 (37%) 1,289 (38%) 996 (42%)
1 11,478 (61%) 1,202 (56%) 1,275 (46%) 1,164 (81%) 2,147 (65%) 2,222 (63%) 2,092 (62%) 1,376 (58%)
DCintra_ddp <0.001
0 16,549 (88%) 1,880 (88%) 2,409 (87%) 1,418 (99%) 2,811 (85%) 3,321 (95%) 3,081 (91%) 1,629 (69%)
1 2,354 (12%) 251 (12%) 366 (13%) 15 (1.0%) 498 (15%) 181 (5.2%) 300 (8.9%) 743 (31%)
DCextra_ddp <0.001
0 10,052 (53%) 1,336 (63%) 1,088 (39%) 1,326 (93%) 1,504 (45%) 2,161 (62%) 1,375 (41%) 1,262 (53%)
1 8,851 (47%) 795 (37%) 1,687 (61%) 107 (7.5%) 1,805 (55%) 1,341 (38%) 2,006 (59%) 1,110 (47%)
DC_ddp <0.001
0 7,698 (41%) 1,085 (51%) 722 (26%) 1,311 (91%) 1,006 (30%) 1,980 (57%) 1,075 (32%) 519 (22%)
1 11,205 (59%) 1,046 (49%) 2,053 (74%) 122 (8.5%) 2,303 (70%) 1,522 (43%) 2,306 (68%) 1,853 (78%)
1 n (%); Median (IQR)
2 Pearson's Chi-squared test; Kruskal-Wallis rank sum test

Radarchart

Création d’un dataset d’apprentissage et d’un dataset de validation

Nous allons maintenant construire l’arbre et l’élaguer

Résultat

On cherche à minimiser l’erreur pour définir le niveau d’élagage

Le graphique ci-dessus affiche le taux de mauvais classement en fonction de la taille de l’arbre. On cherche à minimiser l’erreur.

Visualisation de l’arbre

On test la prédiction de l’arbre

Matrice de confusion arbre de décision

On test le modèle avec le jeux de donnée test

On utilise maintenant le randomForest pour le deuxième modèle avec 1500 arbres

Matrice de confusion random Forest

On test le modèle avec le jeux de donnée test pour le randomForest

On sauvgarde le model random forest

Exemple d’utilisation du modèle

On joint le dataframe “test.df_clean avec la colonne durée”t2d_d"

Je renomme la colonne nouvellement ajouté avec le bon nom et on le place au bonne endroit

Estimation de survie de Kaplan-Meier

---
title: "Final_notebook_paradise"
output:
  html_notebook: default
  html_document:
    df_print: paged
  word_document: default
  pdf_document: default
---

```{r}
#code pour faire disparaitre les lignes de code sur le Preview Notebook
knitr::opts_chunk$set(eval = TRUE, echo = FALSE, results = "hide")
```


```{r}
# Import du dataset Paradise 
data_brut <- read.csv("paradise_2010_2019_sifr_bio_6diags_surv_0522_insee.csv")
```


```{r}
# Copie du dataset
df <- data_brut
str(df)
```



Visualisation des distributions du dataset Paradise avant nettoyage percentiles 
```{r}
# Utilisation de la librairie summarytools pour voir la distribution des variables ainsi que les valeurs manquantes
library(summarytools)
# view(dfSummary(df, varnumbers = FALSE))

print(dfSummary(df, plain.ascii = FALSE, valid.col = FALSE, graph.magnif = 0.76), method='render')


```



```{r}
# Préparation du dataset pour le clustering : transformation des entiers et chaînes de caractères en facteur
df$Trt_Insuline <- as.factor((df$Trt_Insuline))
df$OEDEME <- as.factor((df$OEDEME))
df$tt6h_broncho_parasymp <- as.factor(df$tt6h_broncho_parasymp)
df$RONCHI <- as.factor(df$RONCHI)
df$RALES <- as.factor(df$RALES)
df$SIBILANTS <- as.factor(df$SIBILANTS)
df$Trt_Statine <- as.factor(df$Trt_Statine)
df$TURGJUG <- as.factor(df$TURGJUG)
df$tt6h_vni <- as.factor(df$tt6h_vni)
df$tth6_vasodil <- as.factor(df$tth6_vasodil)
df$CoMorbidite_Non.Cardio.Vasculaire_Anemie <- as.factor(df$CoMorbidite_Non.Cardio.Vasculaire_Anemie)
df$CoMorbidite_CardioVasculaire__Angio <- as.factor(df$CoMorbidite_CardioVasculaire__Angio)
df$CoMorbidite_CardioVasculaire_ICC <- as.factor(df$CoMorbidite_CardioVasculaire_ICC)
df$CoMorbidite_CardioVasculaire__Arterite <- as.factor(df$CoMorbidite_CardioVasculaire__Arterite)
df$CoMorbidite_Non.Cardio.Vasculaire_Asthme <- as.factor(df$CoMorbidite_Non.Cardio.Vasculaire_Asthme)
df$CoMorbidite_CardioVasculaire__AVC <- as.factor(df$CoMorbidite_CardioVasculaire__AVC)
df$CoMorbidite_Non.Cardio.Vasculaire_BPCO <- as.factor(df$CoMorbidite_Non.Cardio.Vasculaire_BPCO)
df$CoMorbidite_CardioVasculaire__Coro <- as.factor(df$CoMorbidite_CardioVasculaire__Coro)
df$CoMorbidite_CardioVasculaire__Defib <- as.factor(df$CoMorbidite_CardioVasculaire__Defib)
df$CoMorbidite_Non.Cardio.Vasculaire_Depression <- as.factor(df$CoMorbidite_Non.Cardio.Vasculaire_Depression)
df$CoMorbidite_CardioVasculaire__Diabete <- as.factor(df$CoMorbidite_CardioVasculaire__Diabete)
df$CoMorbidite_CardioVasculaire__Dyslip <- as.factor(df$CoMorbidite_CardioVasculaire__Dyslip)
df$CoMorbidite_CardioVasculaire__Embol <- as.factor(df$CoMorbidite_CardioVasculaire__Embol)
df$CoMorbidite_CardioVasculaire__FA <- as.factor(df$CoMorbidite_CardioVasculaire__FA)
df$CoMorbidite_CardioVasculaire__Fumeur <- as.factor(df$CoMorbidite_CardioVasculaire__Fumeur)
df$Trt_Furosemide <- as.factor(df$Trt_Furosemide)
df$CoMorbidite_CardioVasculaire__HTA <- as.factor(df$CoMorbidite_CardioVasculaire__HTA)
df$CoMorbidite_Non.Cardio.Vasculaire_IRC <- as.factor(df$CoMorbidite_Non.Cardio.Vasculaire_IRC)
df$CoMorbidite_CardioVasculaire__Obesite <- as.factor(df$CoMorbidite_CardioVasculaire__Obesite)
df$CoMorbidite_CardioVasculaire__Pacemaker <- as.factor(df$CoMorbidite_CardioVasculaire__Pacemaker)
df$CoMorbidite_CardioVasculaire__Resync <- as.factor(df$CoMorbidite_CardioVasculaire__Resync)
df$CoMorbidite_CardioVasculaire__SCAST. <- as.factor(df$CoMorbidite_CardioVasculaire__SCAST.)
df$CoMorbidite_CardioVasculaire__Valvulo <- as.factor(df$CoMorbidite_CardioVasculaire__Valvulo)
df$Cardiopathies <- as.factor(df$Cardiopathies)
df$ICC <- as.factor(df$ICC)
df$accord_Diag <- as.factor(df$accord_Diag)
df$DCintra_ddp <- as.factor(df$DCintra_ddp)
df$DCextra_ddp <- as.factor(df$DCextra_ddp)
df$URG_Diag <- as.factor(df$URG_Diag)
df$PMSI_Diag <- as.factor(df$PMSI_Diag)
df$DC_ddp  <- as.factor(df$DC_ddp)
df$Trt_Ara2 <- as.factor(df$Trt_Ara2)
df$Trt_AntiAgr <- as.factor(df$Trt_AntiAgr)
df$sexe <- as.factor(df$sexe)
df$Trt_AntiCoag <- as.factor(df$Trt_AntiCoag)
df$tt6h_broncho_beta2 <- as.factor(df$tt6h_broncho_beta2)
df$Trt_Bumetanide <- as.factor(df$Trt_Bumetanide)
df$Trt_BetaBloquants <- as.factor(df$Trt_BetaBloquants)
df$tt6h_diur <- as.factor(df$tt6h_diur)
df$Trt_IEC <- as.factor(df$Trt_IEC)
str(df)
```


```{r}
# On supprime la variable « num_resurgences » car ce sont des valeurs uniques donc ils ne seront pas utiles pour faire du clustering.
df_clean <- df[-1]
str(df_clean)
```


```{r}
# On supprime la variable "t2d_d" car variable continue
df_clean <- df_clean[-79]
str(df_clean)
```

```{r}
# on supprimme la variable CoMorbidite_CardioVasculaire_ICC  car on a une variable ICC
df_clean <-df_clean[-23]
str(df_clean)
```


```{r}
# On supprimme toutes les variables avec un pourcentage supérieur à 40 % de valeur manquantes
df_clean <-df_clean[, colSums(is.na(df_clean)) < nrow(df_clean) * 0.4]
```


Nettoyage des percentils (20 variables concernées)


```{r}
# Modification de la distribution pour le CL
library(dplyr)
per <- quantile(df_clean$CL, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- df_clean %>%
  mutate(CL = case_when(CL < per[1] ~ per[1],
              CL> per[2] ~ per[2],
              TRUE ~ CL))
```


```{r}
# Modification de la distribution pour l'Hemoglobine
per <- quantile(test.df_clean$Hemoglobine, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Hemoglobine = case_when(Hemoglobine < per[1] ~ per[1],
              Hemoglobine> per[2] ~ per[2],
              TRUE ~ Hemoglobine))
```


```{r}
# Modification de la distribution pour les Globules_rouges
per <- quantile(test.df_clean$Globules_rouges, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Globules_rouges = case_when(Globules_rouges> per[2] ~ per[2],
              TRUE ~ Globules_rouges))
```


```{r}
# Modification de la distribution pour le CVGR
per <- quantile(test.df_clean$CVGR, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(CVGR = case_when(CVGR < per[1] ~ per[1],
              CVGR> per[2] ~ per[2],
              TRUE ~ CVGR))
```


```{r}
# Modification de la distribution pour les Globules_blancs
per <- quantile(test.df_clean$Globules_blancs, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Globules_blancs = case_when(Globules_blancs> per[2] ~ per[2],
              TRUE ~ Globules_blancs))
```


```{r}
# Modification de la distribution pour les P_neutrophiles
per <- quantile(test.df_clean$P_neutrophiles, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(P_neutrophiles = case_when(P_neutrophiles> per[2] ~ per[2],
              TRUE ~ P_neutrophiles))
```


```{r}
# Modification de la distribution pour les Lymphocytes
per <- quantile(test.df_clean$Lymphocytes, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Lymphocytes = case_when(Lymphocytes> per[2] ~ per[2],
              TRUE ~ Lymphocytes))
```


```{r}
# Modification de la distribution pour les Monocytes
per <- quantile(test.df_clean$Monocytes, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Monocytes = case_when(Monocytes> per[2] ~ per[2],
              TRUE ~ Monocytes))
```


```{r}
# Modification de la distribution pour les Plaquettes
per <- quantile(test.df_clean$Plaquettes, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Plaquettes = case_when(Plaquettes> per[2] ~ per[2],
              TRUE ~ Plaquettes))
```


```{r}
# Modification de la distribution pour la Creat_SI
per <- quantile(test.df_clean$Creat_SI, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Creat_SI = case_when(Creat_SI> per[2] ~ per[2],
              TRUE ~ Creat_SI))
```


```{r}
# Modification de la distribution pour le Glucose
per <- quantile(test.df_clean$Glucose, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Glucose = case_when(Glucose < per[1] ~ per[1],
              Glucose> per[2] ~ per[2],
              TRUE ~ Glucose))
```


```{r}
# Modification de la distribution pour l'HCO3_reels
per <- quantile(test.df_clean$HCO3_reels, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(HCO3_reels = case_when(HCO3_reels < per[1] ~ per[1],
              HCO3_reels> per[2] ~ per[2],
              TRUE ~ HCO3_reels))
```


```{r}
# Modification de la distribution pour l'Hematocrite
per <- quantile(test.df_clean$Hematocrite, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Hematocrite = case_when(Hematocrite < per[1] ~ per[1],
              Hematocrite> per[2] ~ per[2],
              TRUE ~ Hematocrite))
```


```{r}
# Modification de la distribution pour le K
per <- quantile(test.df_clean$K, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(K = case_when(K < per[1] ~ per[1],
              K> per[2] ~ per[2],
              TRUE ~ K))
```


```{r}
# Modification de la distribution pour le LACT
per <- quantile(test.df_clean$LACT, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(LACT = case_when(LACT> per[2] ~ per[2],
              TRUE ~ LACT))
```


```{r}
# Modification de la distribution pour le NA.
per <- quantile(test.df_clean$NA., probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(NA. = case_when(NA. < per[1] ~ per[1],
              NA.> per[2] ~ per[2],
              TRUE ~ NA.))
```


```{r}
# Modification de la distribution pour la PCO2_corrigee
per <- quantile(test.df_clean$PCO2_corrigee, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(PCO2_corrigee = case_when(PCO2_corrigee < per[1] ~ per[1],
              PCO2_corrigee> per[2] ~ per[2],
              TRUE ~ PCO2_corrigee))
```


```{r}
# Modification de la distribution pour le PH_corrige
per <- quantile(test.df_clean$PH_corrige, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(PH_corrige = case_when(PH_corrige < per[1] ~ per[1],
              PH_corrige> per[2] ~ per[2],
              TRUE ~ PH_corrige))
```


```{r}
# Modification de la distribution pour la PO2_corrige
per <- quantile(test.df_clean$PO2_corrige, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(PO2_corrige = case_when(PO2_corrige> per[2] ~ per[2],
              TRUE ~ PO2_corrige))
```


```{r}
# Modification de la distribution pour l' Uree
per <- quantile(test.df_clean$Uree, probs = c(0.01, 0.99),na.rm=TRUE)
test.df_clean <- test.df_clean %>%
  mutate(Uree = case_when(Uree> per[2] ~ per[2],
              TRUE ~ Uree))
```

Visualisation des distributions du dataset Paradise après nettoyage percentiles (1% de part et d'autre)
```{r}
library(summarytools)
# view(dfSummary(df, varnumbers = FALSE))

print(dfSummary(test.df_clean, plain.ascii = FALSE, valid.col = FALSE, graph.magnif = 0.76), method='render')
```


```{r}
# On utilise la librairie VarSelLCM pour du clustering

library(VarSelLCM)
```


```{r}
# clustering sans sélection de variable
set.seed(01062022)
res_without <- VarSelCluster(test.df_clean, gvals = 1:10, vbleSelec = FALSE, crit.varsel = "BIC")
```


Clustering avec sélection de variable (intérvale 1 à 10)
```{r}
# clustering avec sélection de variable
set.seed(01062022)
res_with <- sapply(1:10, VarSelCluster, x=test.df_clean[,1:65], nbcores = 50, crit.varsel = "BIC")

par(mar = c(4, 4, .1, .1))
plot(sapply(res_with, function(u) u@criteria@BIC), type = "b", xlab = "nb clusters", ylab ="BIC")
plot(sapply(res_with, function(u) u@criteria@ICL), type = "b", xlab = "nb clusters", ylab ="ICL")
```

```{r}
set.seed(01062022)
varsel <- VarSelCluster(x=test.df_clean[,1:65], gvals = 7, vbleSelec = TRUE, crit.varsel = "BIC",initModel = 50, nbcores = 50, nbSmall = 250, iterSmall = 20, nbKeep = 50, iterKeep = 1000, tolKeep = 10^(-6))
```




```{r}
# Partition pour 7 cluster
clustering_7 <- varsel
partition_7 <- clustering_7@partitions@zMAP
table(partition_7)
```

Voici les variables les plus discriminantes 
```{r}
# résumer des résultats 
library(summarytools)
library(ggplot2)
summary(clustering_7)

dp <- clustering_7@criteria@discrim
df.dp <- data.frame(Variables = names(dp), Discrim.Power = dp)
df.dp <- subset(df.dp, df.dp$Discrim.Power > 2000)



df.dp$Variables <- factor(df.dp$Variables, levels = df.dp$Variables)
df.dp %>%
ggplot(aes(x = Variables, y = Discrim.Power)) + geom_bar(stat = "identity") + coord_flip()
```


Clustering: Avec 5 clusters

```{r}
set.seed(01062022)
varsel5 <- VarSelCluster(x=test.df_clean[,1:65], gvals = 5, vbleSelec = TRUE, crit.varsel = "BIC",initModel = 50, nbcores = 50, nbSmall = 250, iterSmall = 20, nbKeep = 50, iterKeep = 1000, tolKeep = 10^(-6))
```




```{r}
# Partition pour 5 cluster
clustering_5 <- varsel5
partition_5 <- clustering_5@partitions@zMAP
table(partition_5)
```

Voici les variables les plus discriminantes 
```{r}
# résumer des résultats 
library(summarytools)
library(ggplot2)
summary(clustering_5)

dp5 <- clustering_7@criteria@discrim
df.dp5 <- data.frame(Variables = names(dp), Discrim.Power = dp)
df.dp5 <- subset(df.dp5, df.dp5$Discrim.Power > 2000)



df.dp5$Variables <- factor(df.dp5$Variables, levels = df.dp5$Variables)
df.dp5 %>%
ggplot(aes(x = Variables, y = Discrim.Power)) + geom_bar(stat = "identity") + coord_flip()
```
```{r}
# Probabilités estimées de classement avec 5 clusters
head(fitted(clustering_5, type="probability"))
```




Probabilités estimées de classement pour notre clustering (nous voyons que les 7 clusters sont plustôt bien équlibré)

```{r}
# Probabilités estimées de classement
head(fitted(clustering_7, type="probability"))

```


Résumé des probabilités de mauvaise classification
```{r}
# Résumé des probabilités de mauvaise classification
plot(clustering_7, type="probs-class")
```
```{r}
#On créer une colone  résultat Cluster
test.df_clean$Clusters <- clustering_7@partitions@zMAP
test.df_clean$Clusters <- as.factor(test.df_clean$Clusters)
test.df_clean
```

Table descriptive des variables en fonction des clusters
```{r}
# table descriptive des variables en fonction des clusters
library(gtsummary)
test.df_clean[,45:65] <- lapply(45:65, function(x) as.numeric(test.df_clean[[x]]))
test.df_clean %>%
tbl_summary(
include = all_of(names(test.df_clean)),
by = "Clusters",
missing = "no"
)%>%
add_overall() %>%
add_p() %>%
bold_labels() %>%
italicize_levels()
```


Radarchart
```{r}
library(fmsb)

# Fonction facilitant la création des radat chart
create_beautiful_radarchart <- function(data, color = "#00AFBB", 
                                        vlabels = colnames(data), vlcex = 0.7,
                                        caxislabels = NULL, title = NULL, ...){
  radarchart(
    data, axistype = 1,
    # Personnaliser le polygone
    pcol = color, pfcol = NULL, plwd = 2, plty = 1,
    # Personnaliser la grille
    cglcol = "grey", cglty = 1, cglwd = 0.8,
    # Personnaliser l'axe
    axislabcol = "grey", 
    # etiquettes des variables
    vlcex = vlcex, vlabels = vlabels,
    caxislabels = caxislabels, title = title, ...
  )
}

vars <- as.character(df.dp$Variables)


# Centrage et reduction de toutes les variables (variables de rang + variables binaires)
dataset <- df_clean %>% mutate(across(where(is.factor), as.numeric))%>%
  select(all_of(vars))
dataset$class <- partition_7
dataset[,vars] <- scale(dataset[,vars])


# Dataframe dfTemp avec les moyennes des variables par groupe (autant de lignes que de clusters, meme nb de colonnes que le Dataframe df)
dfTemp <- dataset %>% group_by(class) %>% summarise_at(vars, function(x){mean(x, na.rm=T)})

# Recherche des valeurs min et max en considerant toutes les valeurs moyennes
range(data.frame(dfTemp[,c(-1)]))
# Indiquer les bornes obtenues précédement (ici 2 et -1)
dfRC <- data.frame(rbind(max=1.5, min=-2.2, dfTemp[,-1]))

# Réduire la marge du graphique à l'aide de par()
op <- par(mar = c(1, 2, 2, 2))
# Créer les graphiques radar
create_beautiful_radarchart(
  # caxislabel et seg à ajuster en fonction des bornes 
  data = dfRC, caxislabels = c(-2.2,-1.5,-1, -0.5, 0, 0.5, 1, 1.5), seg = 7,
  color = c("#008000", "#00FFFF", "#008080", "#0000FF", "#000080", "#FF00FF", "#800080"))
# Ajouter une légende
legend(
  x = "topright", xjust = 0, legend = paste0("Cluster ",1:length(unique(dataset$class))," (n=",table(dataset$class),")"), horiz = FALSE,
  bty = "n", pch = 20 , col = c("#008000", "#00FFFF", "#008080", "#0000FF", "#000080", "#FF00FF", "#800080"),
  text.col = "black", cex = 1, pt.cex = 2
)
par(op)
```

Création d’un dataset d’apprentissage et d’un dataset de validation
```{r}
nb_lignes <- floor((nrow(test.df_clean)*0.7)) #Nombre de lignes de l’échantillon d’apprentissage : 75% du dataset
numero_aux_lignes <- test.df_clean[sample(nrow(test.df_clean)), ] #Ajout de numéros de lignes
test.df_clean.train <- test.df_clean[1:nb_lignes, ] #Echantillon d’apprentissage
test.df_clean.test <- test.df_clean[(nb_lignes+1):nrow(test.df_clean), ] #Echantillon de test
```

Nous allons maintenant construire l’arbre et l’élaguer
```{r}
#Construction de l’arbre
library(rpart)
tree <- rpart(Clusters~.,data=test.df_clean.train, minbucket=10, cp=0, xval=10)
```

Résultat
```{r}
#Affichage du résultat
plot(tree, uniform=TRUE, branch=0.5, margin=0.1)
text(tree, all=FALSE, use.n=TRUE)
```
On cherche à minimiser l’erreur pour définir le niveau d’élagage
```{r}
#On cherche à minimiser l’erreur pour définir le niveau d’élagage
plotcp(tree)
```
Le graphique ci-dessus affiche le taux de mauvais classement en fonction de la taille de l’arbre. On cherche à minimiser l’erreur.

```{r}
#Affichage du cp optimal
print(tree$cptable[which.min(tree$cptable[,4]),1])
```
```{r}
# Selection de l'arbre optimal en indiquant l'index de tree$cptable correspondant ici 1
resTreeOpti <- prune(tree, cp=tree$cptable[which.min(tree$cptable[,4]),1][1])
```

Visualisation de l'arbre

```{r}
library(rpart.plot)
# Visualisation de l'arbre
rpart.plot(resTreeOpti, type = 4)
```


On test la prédiction de l'arbre

```{r}
table_mat.tree <- table(test.df_clean.train$Clusters, stats::predict(resTreeOpti, type ="class"))
table_mat.tree
```
```{r}

accuracy_test.tree <- sum(diag(table_mat.tree)/sum(table_mat.tree))
print(paste('Accuracy for test', accuracy_test.tree))
#Explication du code: sum(diag(table_mat)): Somme de la diagonale ;  sum(table_mat) : Somme de la matrice
```

Matrice de confusion arbre de décision
```{r}
# Matrice de confusion arbre de décision
confusionMatrix(test.df_clean.train$Clusters, stats::predict(tree, type ="class"))
```
On test le modèle avec le jeux de donnée test

```{r}
confusionMatrix(test.df_clean.test$Clusters, stats::predict(tree,test.df_clean.test, type ="class"))
```

On utilise maintenant le randomForest pour le deuxième modèle avec  1500 arbres 
```{r}
library(randomForest)
random_f <- randomForest(Clusters~., data=test.df_clean.train, ntree = 1500,
                         importance= TRUE, na.action = na.roughfix)
```

```{r}
print(random_f)
```
Matrice de confusion random Forest
```{r}
# Matrice de confusion random Forest
confusionMatrix(test.df_clean.train$Clusters, stats::predict(random_f, type ="class"))
```
On test le modèle avec le jeux de donnée test pour le randomForest
```{r}
confusionMatrix(test.df_clean.test$Clusters, stats::predict(random_f,test.df_clean.test, type ="class"))
```


On sauvgarde le model random forest
```{r}
saveRDS(random_f,"clustering_model.rds")
```

# Exemple d'utilisation du modèle

```{r}
new_data <- data.frame(
  "sexe"= F,
  "age"= 78,
  "Creat_SI"= 200,
  "Uree"= 1.20,
  "LACT"=5.6,
  "PCO2_corrigee"=65.8,
  "Globules_blancs"= 15.23,
  "HCO3_reels"=45.6,
  "P_neutrophiles"=10.56,
  "PH_corrige"=7.23,
  "ICC"=0,
  "Glucose"=3.78,
  "Trt_Furosemide"=1,
  "CVGR"=14.3,
  "PO2_corrigee"=130.5
)
new_data
```

```{r}
pred <- stats::predict(random_f,newdata = test.df_clean, type = "prob")
pred
```

On joint le dataframe "test.df_clean avec la colonne durée "t2d_d"
```{r}
datajoin <- bind_cols(test.df_clean, data_brut$t2d_d)
```
Je renomme la colonne nouvellement ajouté avec le bon nom et on le place au bonne endroit
```{r}
colnames(datajoin)[72] <- "t2d_d"

datajoin <- datajoin[,c(1:67,72,68,69,70,71)]
```


Estimation de survie de Kaplan-Meier
```{r}
fit <- survfit(Surv(t2d_d, DC_ddp) ~ Clusters, data = datajoin) 
print(fit)
```
```{r}
# Résumé des courbes de survie
summary(fit)
```


```{r}
# Accès au tableau récapitulatif de tri
summary(fit)$table
```

```{r}
d <- data.frame(time = fit$time,
                  n.risk = fit$n.risk,
                  n.event = fit$n.event,
                  n.censor = fit$n.censor,
                  surv = fit$surv,
                  upper = fit$upper,
                  lower = fit$lower
                  )
head(d)
```

```{r}
# Change color, linetype by strata, risk.table color by strata
ggsurvplot(fit,
          pval = TRUE, conf.int = TRUE,
          risk.table = TRUE, # Add risk table
          risk.table.col = "strata", # Change risk table color by groups
          linetype = "strata", # Change line type by groups
          surv.median.line = "hv", # Specify median survival
          ggtheme = theme_bw(), # Change ggplot2 theme
          palette = c("#E7B800", "#2E9FDF"))
```

